响应性、工具性控制与影响的激励
Incentives for Responsiveness, Instrumental Control and Impact
AI总结：
本文提出三个智能体激励概念（响应性、工具性控制与影响），建立图形准则，并讨论其推广至多决策设置，以促进安全公平的智能体行为。
摘要：
我们引入了三个描述智能体激励的概念：响应性激励指示环境中哪些变量（如敏感的人口统计信息）在最优策略下影响决策。工具性控制激励指示智能体的策略是否被选择用来操纵其环境的某一部分，例如用户的偏好或指令。影响激励指示智能体将有意或无意地影响哪些变量。对于每个概念，我们建立了可靠且完备的图形准则，并讨论了可用于产生安全和公平智能体行为的激励的一般技术类别。最后，我们概述了这些概念如何推广到多决策设置。这篇期刊长度的论文扩展了我们的会议论文《响应性、工具性控制与影响的激励》和《智能体激励：因果视角》：关于响应性激励和工具性控制激励的材料已更新，而关于影响激励和多决策设置的工作则是全新的。
英文摘要：
We introduce three concepts that describe an agent's incentives: response incentives indicate which variables in the environment, such as sensitive demographic information, affect the decision under the optimal policy. Instrumental control incentives indicate whether an agent's policy is chosen to manipulate part of its environment, such as the preferences or instructions of a user. Impact incentives indicate which variables an agent will affect, intentionally or otherwise. For each concept, we establish sound and complete graphical criteria, and discuss general classes of techniques that may be used to produce incentives for safe and fair agent behaviour. Finally, we outline how these notions may be generalised to multi-decision settings. This journal-length paper extends our conference publications "Incentives for Responsiveness, Instrumental Control and Impact" and "Agent Incentives: A Causal Perspective": the material on response incentives and instrumental control incentives is updated, while the work on impact incentives and multi-decision settings is entirely new.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
